Overview

Dataset statistics

Number of variables27
Number of observations10000
Missing cells60000
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory216.0 B

Variable types

Numeric11
Categorical10
Unsupported6

Alerts

UPN has a high cardinality: 8466 distinct valuesHigh cardinality
EntryDate has a high cardinality: 381 distinct valuesHigh cardinality
EnrolStatus is highly imbalanced (59.2%)Imbalance
TermlySessionsUnauthorised is highly imbalanced (52.9%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
TermlySessionsAuthorised has 1108 (11.1%) zerosZeros
T_Reason_I has 515 (5.1%) zerosZeros
T_Reason_M has 2088 (20.9%) zerosZeros
T_Reason_S has 246 (2.5%) zerosZeros
T_Reason_T has 2399 (24.0%) zerosZeros
T_Reason_E has 1870 (18.7%) zerosZeros
T_Reason_C has 821 (8.2%) zerosZeros
T_Reason_G has 2024 (20.2%) zerosZeros
T_Reason_O has 538 (5.4%) zerosZeros

Reproduction

Analysis started2023-06-26 14:06:05.062761
Analysis finished2023-06-26 14:06:37.117372
Duration32.05 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9627
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188989.9
Minimum70935
Maximum293861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:37.253153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70935
5-th percentile131680.8
Q1165190.5
median189063
Q3213070.75
95-th percentile247181.15
Maximum293861
Range222926
Interquartile range (IQR)47880.25

Descriptive statistics

Standard deviation35240.095
Coefficient of variation (CV)0.1864655
Kurtosis-0.10287921
Mean188989.9
Median Absolute Deviation (MAD)23927.5
Skewness-0.021856215
Sum1.889899 × 109
Variance1.2418643 × 109
MonotonicityNot monotonic
2023-06-26T15:06:37.581307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201588 3
 
< 0.1%
230185 3
 
< 0.1%
177894 3
 
< 0.1%
145128 3
 
< 0.1%
174391 3
 
< 0.1%
213330 3
 
< 0.1%
205158 3
 
< 0.1%
213973 3
 
< 0.1%
187220 3
 
< 0.1%
223624 2
 
< 0.1%
Other values (9617) 9971
99.7%
ValueCountFrequency (%)
70935 1
< 0.1%
73913 1
< 0.1%
74243 1
< 0.1%
74253 1
< 0.1%
74425 1
< 0.1%
74938 1
< 0.1%
75052 1
< 0.1%
76770 1
< 0.1%
77109 1
< 0.1%
78374 1
< 0.1%
ValueCountFrequency (%)
293861 1
< 0.1%
293626 1
< 0.1%
293038 1
< 0.1%
292556 1
< 0.1%
291252 1
< 0.1%
291115 1
< 0.1%
290916 1
< 0.1%
290598 1
< 0.1%
290294 1
< 0.1%
289495 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8466
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
9eb8a424-6cdc-40bb-b844-cec8ef1f6f33
 
4
7782f03f-6e27-4cf8-b860-cb3729c32e5a
 
4
ce4b57a5-15eb-4327-ab22-088e92dbc40e
 
4
5603f46e-d90f-46cc-9b50-a11eee6f1d3b
 
4
9e959a19-7e83-4954-90ad-457e534ea90d
 
4
Other values (8461)
9980 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7109 ?
Unique (%)71.1%

Sample

1st row8e723ac2-3b26-497b-b5a9-601a01de2bfc
2nd rowb13d4cd0-492b-46d7-bc6f-f9e0d8ca2dc2
3rd row570fcc9e-3ed9-41a8-9c72-962ae82e4158
4th row6d69c5f6-0e16-423f-8998-35ee0bc5f4d6
5th row45d9387d-8872-4181-bf76-de60d6c44185

Common Values

ValueCountFrequency (%)
9eb8a424-6cdc-40bb-b844-cec8ef1f6f33 4
 
< 0.1%
7782f03f-6e27-4cf8-b860-cb3729c32e5a 4
 
< 0.1%
ce4b57a5-15eb-4327-ab22-088e92dbc40e 4
 
< 0.1%
5603f46e-d90f-46cc-9b50-a11eee6f1d3b 4
 
< 0.1%
9e959a19-7e83-4954-90ad-457e534ea90d 4
 
< 0.1%
ec2930da-189f-4ad2-8a1a-6dcf87bc3678 4
 
< 0.1%
a05923ec-ec70-44eb-99a7-f0bd78df253d 4
 
< 0.1%
0a685410-b1a9-4c58-b4f8-8d3e7f173cba 4
 
< 0.1%
a6b24ba5-88bf-4983-aa67-cecd3a567e6b 4
 
< 0.1%
cce63be2-4ee7-4860-9f96-7f75ce967d0d 4
 
< 0.1%
Other values (8456) 9960
99.6%

Length

2023-06-26T15:06:37.837136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9eb8a424-6cdc-40bb-b844-cec8ef1f6f33 4
 
< 0.1%
a6b24ba5-88bf-4983-aa67-cecd3a567e6b 4
 
< 0.1%
7782f03f-6e27-4cf8-b860-cb3729c32e5a 4
 
< 0.1%
53589977-12ca-43c7-babc-90d99b490894 4
 
< 0.1%
794d5f8a-7d75-4149-b9c7-e7d553f9aced 4
 
< 0.1%
8b07b5f7-f384-46d7-a91c-11f1142d5377 4
 
< 0.1%
cce63be2-4ee7-4860-9f96-7f75ce967d0d 4
 
< 0.1%
77bfd15a-6fc2-4e57-bce1-eee6dee475c3 4
 
< 0.1%
0a685410-b1a9-4c58-b4f8-8d3e7f173cba 4
 
< 0.1%
a05923ec-ec70-44eb-99a7-f0bd78df253d 4
 
< 0.1%
Other values (8456) 9960
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28961
 
8.0%
8 21341
 
5.9%
a 21287
 
5.9%
9 21035
 
5.8%
b 20951
 
5.8%
e 19024
 
5.3%
c 18973
 
5.3%
5 18804
 
5.2%
3 18800
 
5.2%
Other values (7) 130824
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202743
56.3%
Lowercase Letter 117257
32.6%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28961
14.3%
8 21341
10.5%
9 21035
10.4%
5 18804
9.3%
3 18800
9.3%
0 18798
9.3%
1 18785
9.3%
6 18784
9.3%
7 18721
9.2%
2 18714
9.2%
Lowercase Letter
ValueCountFrequency (%)
a 21287
18.2%
b 20951
17.9%
e 19024
16.2%
c 18973
16.2%
d 18614
15.9%
f 18408
15.7%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242743
67.4%
Latin 117257
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28961
11.9%
8 21341
8.8%
9 21035
8.7%
5 18804
7.7%
3 18800
7.7%
0 18798
7.7%
1 18785
7.7%
6 18784
7.7%
7 18721
7.7%
Latin
ValueCountFrequency (%)
a 21287
18.2%
b 20951
17.9%
e 19024
16.2%
c 18973
16.2%
d 18614
15.9%
f 18408
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28961
 
8.0%
8 21341
 
5.9%
a 21287
 
5.9%
9 21035
 
5.8%
b 20951
 
5.8%
e 19024
 
5.3%
c 18973
 
5.3%
5 18804
 
5.2%
3 18800
 
5.2%
Other values (7) 130824
36.3%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M
5164 
F
4836 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 5164
51.6%
F 4836
48.4%

Length

2023-06-26T15:06:38.054333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:38.319989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 5164
51.6%
f 4836
48.4%

Most occurring characters

ValueCountFrequency (%)
M 5164
51.6%
F 4836
48.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5164
51.6%
F 4836
48.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5164
51.6%
F 4836
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5164
51.6%
F 4836
48.4%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
7888 
Leaver
1995 
M
 
103
S
 
14

Length

Max length6
Median length1
Mean length1.9975
Min length1

Characters and Unicode

Total characters19975
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeaver
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 7888
78.9%
Leaver 1995
 
20.0%
M 103
 
1.0%
S 14
 
0.1%

Length

2023-06-26T15:06:38.497689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:38.855235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 7888
78.9%
leaver 1995
 
20.0%
m 103
 
1.0%
s 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 7888
39.5%
e 3990
20.0%
L 1995
 
10.0%
a 1995
 
10.0%
v 1995
 
10.0%
r 1995
 
10.0%
M 103
 
0.5%
S 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
50.1%
Lowercase Letter 9975
49.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 7888
78.9%
L 1995
 
20.0%
M 103
 
1.0%
S 14
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 3990
40.0%
a 1995
20.0%
v 1995
20.0%
r 1995
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19975
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 7888
39.5%
e 3990
20.0%
L 1995
 
10.0%
a 1995
 
10.0%
v 1995
 
10.0%
r 1995
 
10.0%
M 103
 
0.5%
S 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 7888
39.5%
e 3990
20.0%
L 1995
 
10.0%
a 1995
 
10.0%
v 1995
 
10.0%
r 1995
 
10.0%
M 103
 
0.5%
S 14
 
0.1%

EntryDate
Categorical

Distinct381
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2011-09-07 00:00:00
689 
2015-09-04 00:00:00
674 
2015-09-03 00:00:00
669 
2014-09-04 00:00:00
 
661
2015-09-01 00:00:00
 
635
Other values (376)
6672 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)1.8%

Sample

1st row2015-09-03 00:00:00
2nd row2015-09-01 00:00:00
3rd row2015-09-03 00:00:00
4th row2013-09-02 00:00:00
5th row2015-09-01 00:00:00

Common Values

ValueCountFrequency (%)
2011-09-07 00:00:00 689
 
6.9%
2015-09-04 00:00:00 674
 
6.7%
2015-09-03 00:00:00 669
 
6.7%
2014-09-04 00:00:00 661
 
6.6%
2015-09-01 00:00:00 635
 
6.3%
2013-09-05 00:00:00 631
 
6.3%
2014-09-03 00:00:00 603
 
6.0%
2012-09-06 00:00:00 584
 
5.8%
2011-09-05 00:00:00 565
 
5.7%
2012-09-04 00:00:00 478
 
4.8%
Other values (371) 3811
38.1%

Length

2023-06-26T15:06:39.135228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2011-09-07 689
 
3.4%
2015-09-04 674
 
3.4%
2015-09-03 669
 
3.3%
2014-09-04 661
 
3.3%
2015-09-01 635
 
3.2%
2013-09-05 631
 
3.2%
2014-09-03 603
 
3.0%
2012-09-06 584
 
2.9%
2011-09-05 565
 
2.8%
Other values (372) 4289
21.4%

Most occurring characters

ValueCountFrequency (%)
0 89437
47.1%
- 20000
 
10.5%
: 20000
 
10.5%
1 13903
 
7.3%
2 12411
 
6.5%
10000
 
5.3%
9 9490
 
5.0%
4 4582
 
2.4%
5 4146
 
2.2%
3 3921
 
2.1%
Other values (3) 2110
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89437
63.9%
1 13903
 
9.9%
2 12411
 
8.9%
9 9490
 
6.8%
4 4582
 
3.3%
5 4146
 
3.0%
3 3921
 
2.8%
6 1199
 
0.9%
7 834
 
0.6%
8 77
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89437
47.1%
- 20000
 
10.5%
: 20000
 
10.5%
1 13903
 
7.3%
2 12411
 
6.5%
10000
 
5.3%
9 9490
 
5.0%
4 4582
 
2.4%
5 4146
 
2.2%
3 3921
 
2.1%
Other values (3) 2110
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89437
47.1%
- 20000
 
10.5%
: 20000
 
10.5%
1 13903
 
7.3%
2 12411
 
6.5%
10000
 
5.3%
9 9490
 
5.0%
4 4582
 
2.4%
5 4146
 
2.2%
3 3921
 
2.1%
Other values (3) 2110
 
1.1%

NCyearActual
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
11
2026 
9
1978 
10
1825 
8
1823 
Leaver
1270 
Other values (3)
1078 

Length

Max length6
Median length2
Mean length2.0899
Min length1

Characters and Unicode

Total characters20899
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeaver
2nd row9
3rd row8
4th row7
5th row11

Common Values

ValueCountFrequency (%)
11 2026
20.3%
9 1978
19.8%
10 1825
18.2%
8 1823
18.2%
Leaver 1270
12.7%
12 684
 
6.8%
7 380
 
3.8%
13 14
 
0.1%

Length

2023-06-26T15:06:39.583272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:39.857313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
11 2026
20.3%
9 1978
19.8%
10 1825
18.2%
8 1823
18.2%
leaver 1270
12.7%
12 684
 
6.8%
7 380
 
3.8%
13 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 6575
31.5%
e 2540
 
12.2%
9 1978
 
9.5%
0 1825
 
8.7%
8 1823
 
8.7%
L 1270
 
6.1%
a 1270
 
6.1%
v 1270
 
6.1%
r 1270
 
6.1%
2 684
 
3.3%
Other values (2) 394
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13279
63.5%
Lowercase Letter 6350
30.4%
Uppercase Letter 1270
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6575
49.5%
9 1978
 
14.9%
0 1825
 
13.7%
8 1823
 
13.7%
2 684
 
5.2%
7 380
 
2.9%
3 14
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 2540
40.0%
a 1270
20.0%
v 1270
20.0%
r 1270
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 1270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13279
63.5%
Latin 7620
36.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6575
49.5%
9 1978
 
14.9%
0 1825
 
13.7%
8 1823
 
13.7%
2 684
 
5.2%
7 380
 
2.9%
3 14
 
0.1%
Latin
ValueCountFrequency (%)
e 2540
33.3%
L 1270
16.7%
a 1270
16.7%
v 1270
16.7%
r 1270
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6575
31.5%
e 2540
 
12.2%
9 1978
 
9.5%
0 1825
 
8.7%
8 1823
 
8.7%
L 1270
 
6.1%
a 1270
 
6.1%
v 1270
 
6.1%
r 1270
 
6.1%
2 684
 
3.3%
Other values (2) 394
 
1.9%

TermlySessionsPossible
Real number (ℝ)

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.697
Minimum39
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:40.129553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile89
Q1108
median118
Q3127
95-th percentile133
Maximum134
Range95
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.852624
Coefficient of variation (CV)0.11973192
Kurtosis1.054176
Mean115.697
Median Absolute Deviation (MAD)9
Skewness-1.0201068
Sum1156970
Variance191.89518
MonotonicityNot monotonic
2023-06-26T15:06:40.405201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 361
 
3.6%
130 355
 
3.5%
132 344
 
3.4%
133 336
 
3.4%
131 333
 
3.3%
128 331
 
3.3%
125 329
 
3.3%
129 323
 
3.2%
124 322
 
3.2%
122 315
 
3.1%
Other values (77) 6651
66.5%
ValueCountFrequency (%)
39 1
< 0.1%
44 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
49 1
< 0.1%
51 1
< 0.1%
52 2
< 0.1%
53 1
< 0.1%
55 2
< 0.1%
ValueCountFrequency (%)
134 196
2.0%
133 336
3.4%
132 344
3.4%
131 333
3.3%
130 355
3.5%
129 323
3.2%
128 331
3.3%
127 303
3.0%
126 361
3.6%
125 329
3.3%

TermlySessionsAuthorised
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8384
Minimum0
Maximum13
Zeros1108
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:40.736579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1951691
Coefficient of variation (CV)0.77338257
Kurtosis0.89356722
Mean2.8384
Median Absolute Deviation (MAD)1
Skewness0.97441937
Sum28384
Variance4.8187673
MonotonicityNot monotonic
2023-06-26T15:06:40.994627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 2161
21.6%
2 1901
19.0%
3 1616
16.2%
4 1129
11.3%
0 1108
11.1%
5 846
 
8.5%
6 531
 
5.3%
7 343
 
3.4%
8 198
 
2.0%
9 84
 
0.8%
Other values (4) 83
 
0.8%
ValueCountFrequency (%)
0 1108
11.1%
1 2161
21.6%
2 1901
19.0%
3 1616
16.2%
4 1129
11.3%
5 846
 
8.5%
6 531
 
5.3%
7 343
 
3.4%
8 198
 
2.0%
9 84
 
0.8%
ValueCountFrequency (%)
13 4
 
< 0.1%
12 16
 
0.2%
11 26
 
0.3%
10 37
 
0.4%
9 84
 
0.8%
8 198
 
2.0%
7 343
 
3.4%
6 531
5.3%
5 846
8.5%
4 1129
11.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8994 
1
1006 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

Length

2023-06-26T15:06:41.196951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:41.360095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8994
89.9%
1 1006
 
10.1%

T_Reason_I
Real number (ℝ)

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6542
Minimum0
Maximum33
Zeros515
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:41.540416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile16
Maximum33
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0823228
Coefficient of variation (CV)0.76377669
Kurtosis0.85774303
Mean6.6542
Median Absolute Deviation (MAD)3
Skewness0.98632453
Sum66542
Variance25.830005
MonotonicityNot monotonic
2023-06-26T15:06:41.768458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 956
 
9.6%
1 930
 
9.3%
3 879
 
8.8%
4 857
 
8.6%
5 782
 
7.8%
6 723
 
7.2%
7 655
 
6.6%
8 616
 
6.2%
0 515
 
5.1%
9 499
 
5.0%
Other values (23) 2588
25.9%
ValueCountFrequency (%)
0 515
5.1%
1 930
9.3%
2 956
9.6%
3 879
8.8%
4 857
8.6%
5 782
7.8%
6 723
7.2%
7 655
6.6%
8 616
6.2%
9 499
5.0%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 1
 
< 0.1%
30 3
 
< 0.1%
29 2
 
< 0.1%
28 3
 
< 0.1%
27 9
 
0.1%
26 8
 
0.1%
25 9
 
0.1%
24 12
0.1%
23 27
0.3%

T_Reason_M
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5307
Minimum0
Maximum8
Zeros2088
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:42.013051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2358844
Coefficient of variation (CV)0.80739818
Kurtosis0.72154673
Mean1.5307
Median Absolute Deviation (MAD)1
Skewness0.84852679
Sum15307
Variance1.5274103
MonotonicityNot monotonic
2023-06-26T15:06:42.251220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3484
34.8%
2 2447
24.5%
0 2088
20.9%
3 1264
 
12.6%
4 514
 
5.1%
5 151
 
1.5%
6 39
 
0.4%
7 12
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 2088
20.9%
1 3484
34.8%
2 2447
24.5%
3 1264
 
12.6%
4 514
 
5.1%
5 151
 
1.5%
6 39
 
0.4%
7 12
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 12
 
0.1%
6 39
 
0.4%
5 151
 
1.5%
4 514
 
5.1%
3 1264
 
12.6%
2 2447
24.5%
1 3484
34.8%
0 2088
20.9%

T_Reason_R
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5982 
1
3893 
2
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

Length

2023-06-26T15:06:42.535437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:42.711296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5982
59.8%
1 3893
38.9%
2 125
 
1.2%

T_Reason_S
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.0879
Minimum0
Maximum62
Zeros246
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:42.881478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q319
95-th percentile32
Maximum62
Range62
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8321636
Coefficient of variation (CV)0.75124073
Kurtosis0.75035047
Mean13.0879
Median Absolute Deviation (MAD)6
Skewness0.96883114
Sum130879
Variance96.671441
MonotonicityNot monotonic
2023-06-26T15:06:43.054865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 505
 
5.1%
2 476
 
4.8%
8 466
 
4.7%
5 460
 
4.6%
6 456
 
4.6%
3 451
 
4.5%
4 448
 
4.5%
7 436
 
4.4%
10 411
 
4.1%
9 404
 
4.0%
Other values (47) 5487
54.9%
ValueCountFrequency (%)
0 246
2.5%
1 505
5.1%
2 476
4.8%
3 451
4.5%
4 448
4.5%
5 460
4.6%
6 456
4.6%
7 436
4.4%
8 466
4.7%
9 404
4.0%
ValueCountFrequency (%)
62 1
 
< 0.1%
55 2
 
< 0.1%
54 3
 
< 0.1%
53 3
 
< 0.1%
52 5
0.1%
51 6
0.1%
50 2
 
< 0.1%
49 6
0.1%
48 7
0.1%
47 9
0.1%

T_Reason_T
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3117
Minimum0
Maximum7
Zeros2399
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:43.596801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0802128
Coefficient of variation (CV)0.82352127
Kurtosis0.63202708
Mean1.3117
Median Absolute Deviation (MAD)1
Skewness0.81073873
Sum13117
Variance1.1668598
MonotonicityNot monotonic
2023-06-26T15:06:43.844363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3877
38.8%
0 2399
24.0%
2 2396
24.0%
3 957
 
9.6%
4 290
 
2.9%
5 70
 
0.7%
6 10
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 2399
24.0%
1 3877
38.8%
2 2396
24.0%
3 957
 
9.6%
4 290
 
2.9%
5 70
 
0.7%
6 10
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 10
 
0.1%
5 70
 
0.7%
4 290
 
2.9%
3 957
 
9.6%
2 2396
24.0%
1 3877
38.8%
0 2399
24.0%

T_Reason_H
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4814 
0
3892 
2
1171 
3
 
121
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

Length

2023-06-26T15:06:44.100285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:44.364172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4814
48.1%
0 3892
38.9%
2 1171
 
11.7%
3 121
 
1.2%
4 2
 
< 0.1%

T_Reason_E
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6331
Minimum0
Maximum8
Zeros1870
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:44.582362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2912208
Coefficient of variation (CV)0.79065629
Kurtosis0.78552855
Mean1.6331
Median Absolute Deviation (MAD)1
Skewness0.87180537
Sum16331
Variance1.6672511
MonotonicityNot monotonic
2023-06-26T15:06:44.771420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3448
34.5%
2 2430
24.3%
0 1870
18.7%
3 1370
 
13.7%
4 602
 
6.0%
5 199
 
2.0%
6 61
 
0.6%
7 16
 
0.2%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 1870
18.7%
1 3448
34.5%
2 2430
24.3%
3 1370
 
13.7%
4 602
 
6.0%
5 199
 
2.0%
6 61
 
0.6%
7 16
 
0.2%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 16
 
0.2%
6 61
 
0.6%
5 199
 
2.0%
4 602
 
6.0%
3 1370
 
13.7%
2 2430
24.3%
1 3448
34.5%
0 1870
18.7%

T_Reason_C
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9619
Minimum0
Maximum18
Zeros821
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:45.033457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0341669
Coefficient of variation (CV)0.76583633
Kurtosis0.72029563
Mean3.9619
Median Absolute Deviation (MAD)2
Skewness0.9688892
Sum39619
Variance9.206169
MonotonicityNot monotonic
2023-06-26T15:06:45.352777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 1527
15.3%
1 1510
15.1%
3 1376
13.8%
4 1133
11.3%
5 964
9.6%
0 821
8.2%
6 762
7.6%
7 587
 
5.9%
8 420
 
4.2%
9 320
 
3.2%
Other values (9) 580
 
5.8%
ValueCountFrequency (%)
0 821
8.2%
1 1510
15.1%
2 1527
15.3%
3 1376
13.8%
4 1133
11.3%
5 964
9.6%
6 762
7.6%
7 587
 
5.9%
8 420
 
4.2%
9 320
 
3.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 3
 
< 0.1%
16 6
 
0.1%
15 21
 
0.2%
14 45
 
0.4%
13 55
 
0.5%
12 104
 
1.0%
11 131
1.3%
10 214
2.1%
9 320
3.2%

T_Reason_G
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5079
Minimum0
Maximum6
Zeros2024
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:45.648659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2037784
Coefficient of variation (CV)0.79831449
Kurtosis0.52727066
Mean1.5079
Median Absolute Deviation (MAD)1
Skewness0.82835902
Sum15079
Variance1.4490825
MonotonicityNot monotonic
2023-06-26T15:06:45.811901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3661
36.6%
2 2441
24.4%
0 2024
20.2%
3 1193
 
11.9%
4 484
 
4.8%
5 161
 
1.6%
6 36
 
0.4%
ValueCountFrequency (%)
0 2024
20.2%
1 3661
36.6%
2 2441
24.4%
3 1193
 
11.9%
4 484
 
4.8%
5 161
 
1.6%
6 36
 
0.4%
ValueCountFrequency (%)
6 36
 
0.4%
5 161
 
1.6%
4 484
 
4.8%
3 1193
 
11.9%
2 2441
24.4%
1 3661
36.6%
0 2024
20.2%

T_Reason_U
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5266 
1
4444 
2
 
288
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

Length

2023-06-26T15:06:46.298585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:47.602775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5266
52.7%
1 4444
44.4%
2 288
 
2.9%
3 2
 
< 0.1%

T_Reason_O
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9177
Minimum0
Maximum29
Zeros538
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:06:48.035366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum29
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4398759
Coefficient of variation (CV)0.75027053
Kurtosis0.69199477
Mean5.9177
Median Absolute Deviation (MAD)3
Skewness0.94045584
Sum59177
Variance19.712498
MonotonicityNot monotonic
2023-06-26T15:06:48.452439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1047
10.5%
2 1012
10.1%
3 987
9.9%
4 932
9.3%
5 887
8.9%
6 784
 
7.8%
7 662
 
6.6%
8 623
 
6.2%
9 542
 
5.4%
0 538
 
5.4%
Other values (20) 1986
19.9%
ValueCountFrequency (%)
0 538
5.4%
1 1047
10.5%
2 1012
10.1%
3 987
9.9%
4 932
9.3%
5 887
8.9%
6 784
7.8%
7 662
6.6%
8 623
6.2%
9 542
5.4%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 3
 
< 0.1%
23 7
 
0.1%
22 15
0.1%
21 19
0.2%
20 23
0.2%

T_Reason_N
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7381 
1
2618 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Length

2023-06-26T15:06:49.086906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:06:49.488500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7381
73.8%
1 2618
 
26.2%
2 1
 
< 0.1%

Interactions

2023-06-26T15:06:32.986717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:08.191507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.015059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.732912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.766401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:15.569345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:18.349923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:21.195892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:24.020150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:27.086202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:29.800268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:33.215947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:08.469831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.130309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.875949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.937582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:15.789386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:18.570294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:21.449777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:24.319731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:27.287595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:29.983051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:33.490317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:08.705742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.257174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:12.015152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.082379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:16.082416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:18.754319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:21.720172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:24.620456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:27.530156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:30.144317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:33.807758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:08.915651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.416447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:12.166857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.259329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:16.305171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:19.005819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:21.912926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:24.934222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:27.771472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:30.448181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:34.039369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.035584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.596317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:12.350301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.412072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:16.489360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:19.256441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:22.218646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:25.199780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:28.018133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:30.702930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:34.272645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.181236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.794312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:12.612639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.579632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:16.652461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:19.550934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:22.463628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:25.563610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:28.275398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:30.906236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:34.613262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.327842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:10.938580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:12.901044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.722111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:16.852860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:19.804122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:22.765377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:25.834211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:28.564610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:31.196770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:34.852420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.461343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.086198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.129733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:14.887181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:17.113983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:20.079780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:23.006575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:26.081009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:28.826130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:31.851161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:35.072543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.610393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.272452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.281096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:15.060334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:17.317340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:20.388488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:23.272559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:26.378443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:29.031202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:32.073322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:35.297187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.756324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.439389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.463388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:15.216690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:17.597964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:20.703824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:23.571236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:26.658237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:29.312133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:32.355743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:35.633372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:09.892380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:11.585605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:13.616631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:15.384466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:17.772679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:20.916469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:23.782943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:26.880119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:29.531288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:06:32.701525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:06:49.724986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossibleTermlySessionsAuthorisedT_Reason_IT_Reason_MT_Reason_ST_Reason_TT_Reason_ET_Reason_CT_Reason_GT_Reason_OGenderEnrolStatusNCyearActualTermlySessionsUnauthorisedT_Reason_RT_Reason_HT_Reason_UT_Reason_N
Estab1.0000.0520.0000.0860.116-0.0750.0020.006-0.0390.022-0.0830.0000.0410.0000.0190.0400.0300.0260.101
TermlySessionsPossible0.0521.000-0.0760.1030.061-0.411-0.009-0.060-0.0520.041-0.0200.0320.1280.1280.0000.0220.0000.0170.036
TermlySessionsAuthorised0.000-0.0761.000-0.143-0.077-0.032-0.002-0.012-0.059-0.011-0.0710.0820.0320.0190.0920.0020.0000.0260.000
T_Reason_I0.0860.103-0.1431.0000.191-0.1320.0130.0340.069-0.0200.0870.0270.0230.0270.0000.0420.0120.0340.017
T_Reason_M0.1160.061-0.0770.1911.000-0.1000.0020.0120.0610.0210.0380.0430.0050.0060.0000.0280.0190.0240.032
T_Reason_S-0.075-0.411-0.032-0.132-0.1001.0000.005-0.050-0.024-0.071-0.0620.0240.1280.2200.0000.0630.0000.0000.010
T_Reason_T0.002-0.009-0.0020.0130.0020.0051.0000.0150.0280.0190.0250.0000.0000.0000.0240.0080.0000.0000.015
T_Reason_E0.006-0.060-0.0120.0340.012-0.0500.0151.0000.1100.0140.1290.0300.0250.0000.0000.0230.0470.0350.000
T_Reason_C-0.039-0.052-0.0590.0690.061-0.0240.0280.1101.0000.0080.1390.0000.0160.0220.0000.0000.0100.0320.000
T_Reason_G0.0220.041-0.011-0.0200.021-0.0710.0190.0140.0081.0000.0410.0000.0080.0030.0000.0260.0280.0020.005
T_Reason_O-0.083-0.020-0.0710.0870.038-0.0620.0250.1290.1390.0411.0000.0000.0360.0000.0000.0730.0000.0950.017
Gender0.0000.0320.0820.0270.0430.0240.0000.0300.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
EnrolStatus0.0410.1280.0320.0230.0050.1280.0000.0250.0160.0080.0360.0001.0000.0930.0000.0320.0000.0000.012
NCyearActual0.0000.1280.0190.0270.0060.2200.0000.0000.0220.0030.0000.0000.0931.0000.0000.0610.0000.0000.000
TermlySessionsUnauthorised0.0190.0000.0920.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0001.0000.0000.0060.0000.000
T_Reason_R0.0400.0220.0020.0420.0280.0630.0080.0230.0000.0260.0730.0000.0320.0610.0001.0000.0000.0100.027
T_Reason_H0.0300.0000.0000.0120.0190.0000.0000.0470.0100.0280.0000.0000.0000.0000.0060.0001.0000.0000.000
T_Reason_U0.0260.0170.0260.0340.0240.0000.0000.0350.0320.0020.0950.0000.0000.0000.0000.0100.0001.0000.000
T_Reason_N0.1010.0360.0000.0170.0320.0100.0150.0000.0000.0050.0170.0000.0120.0000.0000.0270.0000.0001.000

Missing values

2023-06-26T15:06:36.011547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:06:36.888526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
01898778e723ac2-3b26-497b-b5a9-601a01de2bfcNaNNaNNaNNaNNaNMNaNLeaver2015-09-03 00:00:00Leaver124309201411611181
1254044b13d4cd0-492b-46d7-bc6f-f9e0d8ca2dc2NaNNaNNaNNaNNaNMNaNC2015-09-01 00:00:0091211031118011711100
2212723570fcc9e-3ed9-41a8-9c72-962ae82e4158NaNNaNNaNNaNNaNMNaNC2015-09-03 00:00:00812120121013211101130
32040076d69c5f6-0e16-423f-8998-35ee0bc5f4d6NaNNaNNaNNaNNaNMNaNC2013-09-02 00:00:007134301311012033011
423941445d9387d-8872-4181-bf76-de60d6c44185NaNNaNNaNNaNNaNFNaNC2015-09-01 00:00:0011114108102202100100
5148342cfa3bc9f-2101-4e0d-9a4a-d5d27aba936bNaNNaNNaNNaNNaNFNaNC2013-09-04 00:00:001210430111411131121120
62124500b6c00bb-c3d4-42ce-881f-d95fbce101e1NaNNaNNaNNaNNaNFNaNC2011-09-07 00:00:001011520722420312051
71325498e927d21-c8a5-4313-8d2b-218dc081a52cNaNNaNNaNNaNNaNMNaNC2014-09-04 00:00:0010131100005210200120
823801291a9a14a-122a-4439-8aea-3f77973724f4NaNNaNNaNNaNNaNFNaNC2014-09-04 00:00:00Leaver1174013401021083011
919767032f2bdab-3c4d-44f9-8f04-e98136270fe5NaNNaNNaNNaNNaNMNaNC2014-09-01 00:00:0012124503211640042190
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
9990172775c261c860-a1aa-4abe-bc7d-0555479cbe60NaNNaNNaNNaNNaNMNaNLeaver2015-09-01 00:00:00897502001021112120
9991173204252d1cb9-58b8-4935-a8e2-7288acf3d246NaNNaNNaNNaNNaNMNaNC2012-09-01 00:00:0010131207201612141190
99921797329adc9e8b-9097-465b-a77c-6759afe0a2faNaNNaNNaNNaNNaNFNaNC2014-09-04 00:00:00813340710110342010
999320968508ec7c6c-5ddc-4de7-8178-0e0c8237cc44NaNNaNNaNNaNNaNMNaNLeaver2014-09-04 00:00:001010930110721000070
99941706877c998a8b-293d-4558-9258-793bf7fcb7e4NaNNaNNaNNaNNaNFNaNC2013-09-05 00:00:001099301101731221010
9995156660482cb923-522d-481d-8299-f1964cc41e16NaNNaNNaNNaNNaNMNaNC2015-09-03 00:00:007131302320602121060
9996181010cd97265d-1a90-4c21-b49b-967e450a97c8NaNNaNNaNNaNNaNMNaNC2011-09-05 00:00:00111051013112002171010
99972080874c8817db-cf4b-45b0-a484-7f72ebf1ee15NaNNaNNaNNaNNaNFNaNC2015-09-03 00:00:0010950012202700121120
9998149303b980fd4f-c65a-4869-9a31-b0fd4269413bNaNNaNNaNNaNNaNMNaNC2015-09-03 00:00:007133103612110310110
999922230879b842c3-bb32-49fe-9c52-e0bc47afa759NaNNaNNaNNaNNaNFNaNLeaver2014-09-02 00:00:001012870920511231000